CONTINUOUS-TIME DISCRETE-SPACE MODELS FOR ANIMAL MOVEMENT

• 成果类型: Article
• 署名作者: Hanks, Ephraim M.; Hooten, Mevin B.; Alldredge, Mat W.
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; United States Department of the Interior; United States Geological Survey; Colorado State University System; Colorado State University Fort Collins
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/14-AOAS803
• 发表日期: 2015
• 页码: 145-165
• 关键词: resource selection; FRAMEWORK; inference; likelihood; predation; ecology; paths; HOME

摘要

The processes influencing animal movement and resource selection are complex and varied. Past efforts to model behavioral changes over time used Bayesian statistical models with variable parameter space, such as reversible-jump Markov chain Monte Carlo approaches, which are computationally demanding and inaccessible to many practitioners. We present a continuous-time discrete-space (CTDS) model of animal movement that can be fit using standard generalized linear modeling (GLM) methods. This CTDS approach allows for the joint modeling of location-based as well as directional drivers of movement. Changing behavior over time is modeled using a varying-coefficient framework which maintains the computational simplicity of a GLM approach, and variable selection is accomplished using a group lasso penalty. We apply our approach to a study of two mountain lions (Puma concolor) in Colorado, USA.